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Interpretable Machine Learning

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보존된 평가 기록: 2

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/eusip/POM

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A multimodal movie review corpus for fine-grained opinion mining

https://github.com/facebookresearch/LASER

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Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

https://github.com/facebookresearch/TorchRay

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Understanding Deep Networks via Extremal Perturbations and Smooth Masks

https://github.com/facebookresearch/muss

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MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases

https://github.com/facebookresearch/nbm-spam

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Scalable Interpretability via Polynomials

https://github.com/fau-is/gam_comparison

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GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

https://github.com/felixfrohnertdb/qcvae

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Explainable Representation Learning of Small Quantum States

https://github.com/foroughp/manipulating-and-measuring-model-interpretability

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Manipulating and Measuring Model Interpretability

https://github.com/fromm-m/aaai2021-am-peer-reviews

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Argument Mining Driven Analysis of Peer-Reviews

https://github.com/fxing79/ibaa

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Discovering Bayesian Market Views for Intelligent Asset Allocation

https://github.com/fzi-forschungszentrum-informatik/TSInterpret

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TSInterpret: A unified framework for time series interpretability

https://github.com/gablabc/partial_order_in_chaos

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Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

https://github.com/gablabc/understand_treeshap

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Understanding Interventional TreeSHAP : How and Why it Works

https://github.com/garfieldlyu/suwr

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Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions

https://github.com/gcskoenig/icpr2020-rfi

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Relative Feature Importance

https://github.com/giangnguyen2412/dissect_catastrophic_forgetting

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Explaining How Deep Neural Networks Forget by Deep Visualization

https://github.com/gim4855744/honam

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Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

https://github.com/gkaramanolakis/ISWD

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Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

https://github.com/goodaycoder/lldprogression

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Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

https://github.com/guojiapub/quadtag

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AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach

https://github.com/hadi-ghnd/scrap-classification

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Ammunition Component Classification Using Deep Learning

https://github.com/hallie304/vlsp23-comparative-opinion-mining

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Unveiling Comparative Sentiments in Vietnamese Product Reviews: A Sequential Classification Framework

https://github.com/haobytes/argsum-datatset

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Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation

https://github.com/harrywilldr/argsum-datatset

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Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation

https://github.com/haugjo/dynamic-model-tree

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Dynamic Model Tree for Interpretable Data Stream Learning

https://github.com/haydenmct/predictive-equivalence

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Leveraging Predictive Equivalence in Decision Trees

https://github.com/hellisotherpeople/debatekg

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DebateKG: Automatic Policy Debate Case Creation with Semantic Knowledge Graphs

https://github.com/hestiri/mlho

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Individualized Prediction of COVID-19 Adverse outcomes with MLHO

https://github.com/hfawaz/ijcars19

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Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks

https://github.com/hkust-knowcomp/tilfa

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TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining

https://github.com/homayunafra/Decision_Theoretic_Approach_for_Interpretability_in_Bayesian_Framework

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A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework

https://github.com/hubuers/diskcd

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Disentangling Heterogeneous Knowledge Concept Embedding for Cognitive Diagnosis on Untested Knowledge

https://github.com/i6092467/semi-supervised-multiview-cbm

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Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis

https://github.com/iamollas/Altruist

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Altruist: Argumentative Explanations through Local Interpretations of Predictive Models

https://github.com/icml24/sparsecbm

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Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning

https://github.com/idiap/fullgrad-saliency

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Full-Gradient Representation for Neural Network Visualization

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